Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	117,527
2	South Dakota	105,997
3	Iowa	83,905
4	Wisconsin	83,532
5	Nebraska	80,163
6	Utah	76,907
7	Rhode Island	73,451
8	Idaho	71,904
9	Tennessee	71,795
10	Wyoming	71,461
11	Montana	71,161
12	Illinois	70,114
13	Kansas	69,901
14	Minnesota	69,498
15	Indiana	67,738
16	Arkansas	65,418
17	Nevada	64,942
18	Alabama	64,383
19	Mississippi	63,978
20	Oklahoma	63,624
21	Missouri	62,270
22	Arizona	61,004
23	New Mexico	60,806
24	Louisiana	60,754
25	Alaska	58,960
26	Florida	55,009
27	Texas	54,212
28	Kentucky	53,796
29	Colorado	53,052
30	Georgia	52,424
31	South Carolina	51,872
32	Ohio	51,831
33	Delaware	50,432
34	Michigan	49,198
35	New Jersey	47,863
36	California	45,798
37	Massachusetts	45,691
38	Connecticut	45,657
39	North Carolina	44,640
40	New York	42,770
41	Pennsylvania	42,525
42	Maryland	40,825
43	West Virginia	38,920
44	District of Columbia	36,987
45	Virginia	35,075
46	Puerto Rico	31,232
47	Washington	29,494
48	New Hampshire	25,711
49	Oregon	23,782
50	Hawaii	14,081
51	Maine	13,641
52	Vermont	10,004

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,456
2	California	1,303
3	Kansas	1,154
4	Alabama	961
5	Indiana	916
6	Utah	912
7	Arkansas	908
8	Nevada	857
9	Oklahoma	822
10	Massachusetts	810
11	Rhode Island	810
12	Mississippi	796
13	Idaho	785
14	New Mexico	785
15	South Dakota	781
16	Arizona	774
17	Pennsylvania	765
18	Ohio	755
19	West Virginia	751
20	Wyoming	745
21	Louisiana	728
22	Georgia	694
23	Connecticut	684
24	Iowa	684
25	Alaska	677
26	Kentucky	675
27	Delaware	673
28	Texas	673
29	North Carolina	663
30	Nebraska	661
31	South Carolina	631
32	Wisconsin	631
33	Missouri	614
34	Illinois	610
35	New Hampshire	592
36	Montana	591
37	Florida	584
38	Colorado	576
39	New York	556
40	New Jersey	519
41	North Dakota	508
42	Washington	460
43	Michigan	458
44	Minnesota	456
45	Virginia	432
46	Maryland	392
47	Maine	391
48	District of Columbia	361
49	Oregon	333
50	Puerto Rico	283
51	Vermont	170
52	Hawaii	93

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,040
2	New York	1,838
3	Massachusetts	1,684
4	North Dakota	1,615
5	Connecticut	1,565
6	Rhode Island	1,533
7	Louisiana	1,504
8	South Dakota	1,503
9	Mississippi	1,462
10	Illinois	1,278
11	Michigan	1,187
12	Iowa	1,093
13	Indiana	1,079
14	Arizona	1,074
15	Pennsylvania	1,067
16	Arkansas	1,040
17	District of Columbia	1,031
18	New Mexico	1,014
19	Florida	949
20	South Carolina	946
21	Georgia	942
22	Maryland	886
23	Nevada	885
24	Texas	883
25	Delaware	877
26	Alabama	876
27	Tennessee	863
28	Minnesota	847
29	Missouri	842
30	Kansas	803
31	Montana	799
32	Wisconsin	796
33	Nebraska	770
34	Colorado	748
35	Idaho	716
36	Ohio	681
37	West Virginia	608
38	Wyoming	606
39	North Carolina	588
40	Kentucky	585
41	California	567
42	Oklahoma	546
43	Virginia	538
44	New Hampshire	469
45	Puerto Rico	417
46	Washington	416
47	Utah	355
48	Oregon	309
49	Alaska	240
50	Maine	209
51	Hawaii	197
52	Vermont	171

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	26
2	South Dakota	25
3	North Dakota	24
4	New Mexico	19
5	Pennsylvania	19
6	Arizona	17
7	Rhode Island	17
8	Illinois	16
9	Tennessee	16
10	Indiana	14
11	Minnesota	14
12	West Virginia	14
13	Arkansas	13
14	Wyoming	13
15	Iowa	12
16	Michigan	12
17	Nevada	12
18	Wisconsin	12
19	Alabama	11
20	Idaho	11
21	Mississippi	11
22	Missouri	11
23	Colorado	10
24	Connecticut	10
25	Delaware	9
26	Montana	9
27	New Jersey	9
28	Texas	9
29	California	8
30	Kentucky	8
31	Maryland	8
32	Massachusetts	8
33	New Hampshire	8
34	Ohio	8
35	Louisiana	7
36	North Carolina	7
37	Oregon	7
38	South Carolina	7
39	Nebraska	6
40	Oklahoma	6
41	Utah	6
42	New York	5
43	Florida	4
44	Georgia	4
45	Puerto Rico	4
46	Virginia	4
47	District of Columbia	3
48	Maine	3
49	Vermont	3
50	Alaska	1
51	Hawaii	1
52	Washington	1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	258,208	1	99
Norton	Kansas	214,699	2	99
Bon Homme	South Dakota	208,520	3	99
Lincoln	Arkansas	208,231	4	99
Buffalo	South Dakota	206,422	5	99
Davidson	Tennessee	86,269	418	86
Richland	South Carolina	57,521	1490	52
York	South Carolina	46,196	2102	33
Orange	California	37,384	2485	20
Pierce	Washington	26,963	2799	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Foster	North Dakota	5,919	4	99
Gregory	South Dakota	5,735	5	99
Richland	South Carolina	765	1685	46
Davidson	Tennessee	713	1784	43
York	South Carolina	587	2032	35
Orange	California	546	2104	33
Pierce	Washington	337	2547	18

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.